This course provides a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. The course begins by showing how to load the data into memory and visualize the time series data to be modeled. Attribute variables are introduced and implemented in the visualization. The course then covers the essentials of using pipelines for generating forecasts and selecting champion pipelines in a project. It also teaches you how to incorporate large-scale forecasting practices into the forecasting project. These include the creation of data hierarchies, forecast reconciliation, overrides, and best practices associated with forecast model selection.
Learn How To
Automatically create and fit custom forecast models using structured analytic workflows or pipelines. Visualize modeling data using attribute variables. Refine forecast models to improve forecast accuracy. Apply overrides-generated forecasts. Generate forecast data sets for deployment. Build and share custom pipelines for large-scale forecasting analyses.Who Should Attend
Forecasters and analysts in any industry, including retail, financial services, manufacturing, and pharmaceuticals
Prerequisites
Although prerequisites are not required, you will get more value from this course if you first attend these two courses: ;
Time Series Modeling Essentials. Statistics You Need to Know for Machine Learning.SAS Products Covered
SAS Visual Forecasting
Course Outline
Introduction and Data Visualization
Lesson overview. Getting started with your forecasting project. Visualizing time series using attribute variables. Pipeline EssentialsLesson overview.Definition and creation of a time series. Fundamental concepts in time series modeling. Classes of time series models. Model comparison using honest assessment. Pipeline templates and pipeline comparison. Interactive modeling node.Hierarchical ForecastingLesson overview. Building a hierarchical forecasting model. Selecting a champion model.Hierarchical forecasting node.Post-forecasting FunctionalityLesson overview. Overrides and exporting generated tables.Creating Better Forecasts and Improving ModelingLesson overview. New and existing attributes and filters. Data export. Score new data and task options. Event variables. Intermittent data and models. Outlier detection. Ensemble node.In-Line Code Access and Overview (Appendix) 
Live Instructor Dates 
